Netlifx

Exploratory Data Analysis (EDA) in the context of loans involves a systematic examination of loan-related datasets. Through statistical techniques, visualizations, and summarization methods, analysts explore patterns, trends, and relationships within the data. EDA can provide insights into factors influencing loan approval, repayment behavior, and overall portfolio performance. This data-driven approach aids in making informed decisions, refining risk models, and optimizing lending strategies for better outcomes in the consumer finance industry.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• The project revolves around a consumer finance company specializing in providing various types of loans to urban customers.
• The primary challenge is to optimize the loan approval process, striking a balance between expanding the customer base and mitigating the risk of financial losses due to defaults.
• The company seeks to improve its ability to identify creditworthy applicants, ensuring it doesn't miss profitable lending opportunities while minimizing the approval of loans to individuals with a high likelihood of default.
• The project utilizes a comprehensive dataset sourced from the consumer finance company's loan records.

Conclusions

1. Exploring Distribution of Key Variables

• Generate histograms and box plots for "Hours Viewed," "Number of Ratings," and "Rating."
• Analyze central tendencies, outliers, and patterns in each variable.

2. Analyzing Distribution of Genres

• Explore genre distribution to understand the popularity of different categories.
• Visualize genre frequency using bar charts or pie charts.

3. Checking Correlations between Numerical Variables

• Use correlation matrices or scatter plots to identify relationships between key variables.
• Interpret correlation coefficients to understand strength and direction.

4. Analyzing Distribution of Releases Over Time

• Create a time-series plot to visualize the distribution of releases over time.
• Look for trends, spikes, or seasonality in release dates.

5. Exploring Distribution of Genres Over Time

• Create stacked area charts or multiple line charts to show genre distribution over time.
• Identify emerging trends or shifts in genre popularity.

6. Analyzing Average Ratings and Hours Viewed for Each Genre

• Calculate average ratings and hours viewed for each genre.
• Visualize using bar charts or box plots to compare genre performance.

7. Identifying Top-Rated Shows

• Rank shows based on ratings and hours viewed.
• Present the top-rated shows using tables or visualizations.

8. Checking Correlation between Number of Ratings and Overall Rating

• Use scatter plots or correlation analysis to explore correlation between number of ratings and overall rating.

9. Exploring Most Common Keywords in Descriptions

• Tokenize and analyze descriptions to identify common keywords.
• Create word clouds or frequency distributions for visualization.

10. Analyzing Keywords and Ratings/Views Associations

• Cross-reference keywords with ratings and views data.
• Conduct statistical analysis or visualizations to determine associations.

Technologies Used

• Python: version 3.x
• Pandas: version 1.3.4
• Seaborn: version 0.12.2
• Matplotlib: version 3.5.3
• Plotly: version 5.9.0
• Numpy: version 1.21.6
• Scipy: version 1.7.3

Acknowledgements

• This case study stands as a collaborative effort, and we are grateful for the collective support, insights, and resources that have contributed to its development.

Contact

Created by :

• Jitesh Rathod